The main idea of the generation-based FSL methods improves the generalizability of the classifier by producing diverse samples. Generative Adversarial Network (GAN) is a typical generator in FSL. For example, Wang \(et\ al\) . [1]} applied GAN to generate samples, which improves performance by about \(6\%\) . And Li \(et\ al\) . [2]} employed cWGAN to ensure the diversity of generated data. Besides, some studies employ auxiliary approaches to directly generate image-level samples. For example, Zhang \(et\ al\) . [3]} introduced saliency-based masks to exchange foregrounds and backgrounds of different images to augment samples.
